Groundwater Dependent Ecosystems (GDEs) concentrate high levels of biodiversity and several species not found anywhere else. They prevail in the landscape through the ecological contribution of groundwater. They, GDEs, are vulnerable to drastic changes in groundwater depth. If, for example, bulk groundwater pumping drastically increases the groundwater depth and GDEs can no longer access it, they would die out. In the Cape Floristic Region (CFR), South Africa, there is limited information about the spatial distribution of groundwater dependent ecosystems. With the CFR having multiple locations with current and subsequent bulk groundwater pumping, identifying the spatial distribution of GDEs is a prerequisite for establishing their groundwater requirements. This dissertation presents a proposed novel method to identify rapid recovering wetlands predicted to be GDEs and uses Random Forest (RF) to predict their spatial distribution. The proposed novel approach leveraged the periodic fire disturbances in the CFR and applied the remote sensing index; Normalised Difference Vegetation Index (NDVI) extracted from high spatial resolution (1 m) aerial orthoimages. The proposed novel approach involves three levels of analysis. The first two levels used a one-way Analysis of Variance (ANOVA) to analyse the sensitivity of mean NDVI to discriminate wetland and non-wetland classes in burned and unburned study sites, and a post-hoc test: Tukey's Honest Significant Differences (HSD) pair-wise comparison to detect differences between the wetland and non-wetland mean NDVI and infer an NDVI threshold of wetland classes. In unburned sites, ANOVAshowed no statistical significance between wetland and non-wetland classes, F (2,15) = 3.53, p = 0.055. In burned sites, however, ANOVA showed there was a significant difference between wetland and non-wetland classes, F (2,15) = 9.66, p = 0.002. ANOVA and Tukey showed there were significant differences betweenwetland and non-wetland classes, with wetlands having between 0.22 and 0.37 greater NDVI than non-wetlands. The last level of analysis employed a kernel density estimator function to assess the recovery rate post-burn and use it to detect faster recovery as potential of wetlands to be GDEs; results showed that potential wetland GDEs experience rapid NDVI recovery > 236 days post-fire. In the fire prone CFR, leveraging fire data to detect GDEs provides a potentially simple and efficient way of building a local database for GDEs. The proposed novel approach showed leveraging fire data is a simple alternative to laborious field data to identify and map GDEs in the CFR. But because of the finite spectral bands in aerial orthoimages, Sentinel-2A multi-epochs dataset was utilised to carry out random forest for predicting the spatial distribution of potential wetland GDEs in the Kogelberg Nature Reserve. Sentinel-2A bands: Short-Wave Infrared (SWIR), NearInfrared (NIR), Red-edge, Red, Green, NDVI and Normalised Difference Wetness Index (NDWI) predictors and the potential wetland GDEs/non-wetland classes as the response. I tuned RF using five-fold repeated spatial cross-validation instead of the typical cross-validation tuning to account for the spatial structure of the data. The overall predictive accuracy of RF was between 59%-71%. This predictive accuracy may have been reduced by the application of spatial cross-validation that accounted for the spatial autocorrelation in the multi-date data. The dissertation showed that Sentinel-2A multi-date data applies in predicting the distribution of potential wetland GDEs but might not be effective for smaller (< 100 m2) wetlands. These small wetlands showed rapid post-fire recovery (less than a year post-fire) and were effectively detected with high resolution aerial orthoimages (1 m) spatial resolution.